SoroushVahidi/scidocs huggingface dataset:
Predicting Price Changes in Ethereum
(2017)

The market capitalization of publicly traded cryptocurrencies is currently above $230 billion (Bovaird, 2017). Bitcoin, the most valuable cryptocurrency, serves primarily as a digital store of value (Van Alstyne, 2014), and its price predictability has been well-studied (Hegazy and Mumford, 2016). However, Ethereum has the second-highest market capitalization and supports much more functionality than Bitcoin. While its price predictability is sparsely covered in published literature, the technology’s additional functionality may cause Ether’s price predictability to differ significantly from that of Bitcoin. These characteristics are outlined in the following subsection; the underlying details of Bitcoin (Nakamoto, 2008) and Ethereum (Buterin, 2013) are elided, as they are described in depth in the cited papers.
Binary malware image classification using machine learning with local binary pattern
(2017)

Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.
The return of AdaBoost.MH: multi-class Hamming trees
(2013)

Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem toK binary one-againstall classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH.
Formalizing agility, part 2: how an agile organization embraced the CMMI
(2006)

Most large IT organizations need the best of both worlds - working software that supports ever-changing business needs, and a process for delivering software that is predictable, trainable, and auditable. Organizations with agile software teams often receive timely, cost-effective solutions of sufficient quality. Organizations with formal processes often benefit from industry-recognized certifications and robust process improvement mechanisms. Rarely does a single, large IT organization achieve both. DTE Energy has such a combination: its lightweight yet CMMI-compatible methodology is now used within its 600+ person IT organization to deliver and support working software. Its small teams embrace core agile principles as they provide "just enough" solutions that satisfy maturing business needs. Yet they passed two SCAMPI appraisals toward formal CMMI Level III accreditation, scheduled for mid-2006. This report extends the Agile 2005 experience report on DTE Energy's agile IT organization's journey toward CMMI staged maturity accreditation. This report briefly recaps their seven years of agile experience, presents governance mechanisms and change management techniques, and highlights their four-release, one-year plan for CMMI Level III accreditation. Finally, this report offers suggestions on embracing a formal process framework that are applicable to any organization
Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor
(2016)

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.
Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model
(2014)

The ABSTRACT is to be in fully-justified italicized text, at the top of the left-hand column, below the author and affiliation information. Use the word “Abstract” as the title, in 12-point Times, boldface type, centered relative to the column, initially capitalized. The abstract is to be in 10point, single-spaced type. Leave two blank lines after the Abstract, then begin the main text. Look at previous CVPR abstracts to get a feel for style and length.
Signal Characteristics on Sensor Data Compression in IoT -An Investigation
(2016)

In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.
Resolving constraint conflicts
(2004)

In this paper, we define constraint conflicts and examine properties that may aid in guiding their resolution. A constraint conflict is an inconsistency between the access control policy and the constraints specified to limit that policy. For example, a policy that permits a high integrity subject to access low integrity data is in conflict with a Biba integrity constraint. Constraint conflicts differ from typical policy conflicts in that constraints are never supposed to be violated. That is, a conflict with a constraint results in a policy compilation error, whereas policy conflicts are resolved at runtime. As we have found in the past, when constraint conflicts occur in a specification a variety of resolutions are both possible and practical. In this paper, we detail some key formal properties of constraint conflicts and show how these are useful in guiding conflict resolution. We use the SELinux example policy for Linux 2.4.19 as the source of our constraint conflicts and resolution examples. The formal properties are used to guide the selection of resolutions and provide a basis for a resolution language that we apply to resolve conflicts in the SELinux example policy.
Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
(2018)

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1.
A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance
(1999)

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.
Co-operative downloading in vehicular ad-hoc wireless networks
(2005)

Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.
Internet Traffic Measurement
(2001)

This tutorial article discusses the role of network traffic measurement in the design, testing, and evaluation of Internet protocols and applications. The article begins with some background information on Internet traffic measurement, and then proceeds to discuss the “tools of the trade”, including examples of both hardwarebased and software-based approaches to network traffic measurement. The article concludes with a summary of the main observations from the past fifteen years of network measurement research, along with pointers to the relevant literature for more information.
Runtime Verification of Ethereum Smart Contracts
(2018)

The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.
DBpedia - A crystallization point for the Web of Data
(2009)

The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.
LUBM: A benchmark for OWL knowledge base systems
(2005)

We describe our method for benchmarking Semantic Web knowledge base systems with respect to use in large OWL applications. We present the Lehigh University Benchmark (LUBM) as an example of how to design such benchmarks. The LUBM features an on-tology for the university domain, synthetic OWL data scalable to an arbitrary size, fourteen extensional queries representing a variety of properties, and several performance metrics. The LUBM can be used to evaluate systems with different reasoning capabilities and storage mechanisms. We demonstrate this with an evaluation of two memory-based systems and two systems with persistent storage.
Automatically refining the wikipedia infobox ontology
(2008)

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.
Exploiting Wikipedia as External Knowledge for Named Entity Recognition
(2007)

We explore the use of Wikipedia as external knowledge to improve named entity recognition (NER). Our method retrieves the corresponding Wikipedia entry for each candidate word sequence and extracts a category label from the first sentence of the entry, which can be thought of as a definition part. These category labels are used as features in a CRF-based NE tagger. We demonstrate using the CoNLL 2003 dataset that the Wikipedia category labels extracted by such a simple method actually improve the accuracy of NER.
Energy Minimization Using Multiple Supply Voltages
(1997)

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data ow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.
Modeling Commonsense Reasoning via Analogical Chaining: A Preliminary Report
(2016)

Understanding the nature of commonsense reasoning is one of the deepest questions of cognitive science. Prior work has proposed analogy as a mechanism for commonsense reasoning, with prior simulations focusing on reasoning about continuous behavior of physical systems. This paper examines how analogy might be used in commonsense more broadly. The two contributions are (1) the idea of common sense units, intermediate-sized collections of facts extracted from experience (including cultural experience) which improves analogical retrieval and simplifies inferencing, and (2) analogical chaining, where multiple rounds of analogical retrieval and mapping are used to rapidly construct explanations and predictions. We illustrate these ideas via an implemented computational model, tested on examples from an independently-developed test of commonsense reasoning.
A key-management scheme for distributed sensor networks
(2002)

Distributed Sensor Networks (DSNs) are ad-hoc mobile networks that include sensor nodes with limited computation and communication capabilities. DSNs are dynamic in the sense that they allow addition and deletion of sensor nodes after deployment to grow the network or replace failing and unreliable nodes. DSNs may be deployed in hostile areas where communication is monitored and nodes are subject to capture and surreptitious use by an adversary. Hence DSNs require cryptographic protection of communications, sensor-capture detection, key revocation and sensor disabling. In this paper, we present a key-management scheme designed to satisfy both operational and security requirements of DSNs. The scheme includes selective distribution and revocation of keys to sensor nodes as well as node re-keying without substantial computation and communication capabilities. It relies on probabilistic key sharing among the nodes of a random graph and uses simple protocols for shared-key discovery and path-key establishment, and for key revocation, re-keying, and incremental addition of nodes. The security and network connectivity characteristics supported by the key-management scheme are discussed and simulation experiments presented.
A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers
(2018)

It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object’s residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.
Digital Watermarking and Steganography
(2007)

Sharing, disseminating, and presenting data in digital format is not just a fad, but it is becoming part of our life. Without careful planning, digitized resources could easily be misused, especially those that are shared across the Internet. Examples of such misuse include use without the owner’s permission, and modification of a digitized resource to fake ownership. One way to prevent such behaviors is to employ some form of copyright protection technique, such as digital watermarks. Digital watermarks refer to the data embedded into a digital source (e.g., images, text, audio, or video recording). They are similar to watermarks in printed materials as a message inserted into the host media typically becomes an integral part of the media. Apart from traditional watermarks in printed forms, digital watermarks may also be invisible, may be in the forms other than graphics, and may be digitally removed.
An evaluation of bipartitioning techniques
(1995)

Logic partitioning is an important issue in VLSI CAD, and has been an area of active research for at least the last 25 years. Numerous approaches have been developed and many different techniques have been combined for a wide range of applications. In this paper, we examine many of the existing techniques for logic bipartitioning and present a methodology for determining the best mix of approaches. The result is a novel bipartitioning algorithm that includes both new and pre-existing techniques. Our algorithm produces results that are at least 16% better than the state-of-the-art while also being efficient in run-time.